Allocation of carbon emission quotas to Chinese power enterprises. (October 2018)
- Record Type:
- Journal Article
- Title:
- Allocation of carbon emission quotas to Chinese power enterprises. (October 2018)
- Main Title:
- Allocation of carbon emission quotas to Chinese power enterprises
- Authors:
- Sun, Huaping
Tariq, Gulzara
Chen, Hui
Zhu, Jin
Liu, Yue
Wu, Chao - Abstract:
- Abstract: It is essential to study the allocation of carbon emission quotas to key high-carbon industries. Here, we used the theoretical generation performance standard (GPS) to evaluate the carbon inputs and outputs of the five major Chinese power groups. We predicted their 2017, 2020, and 2030 performances using a grey prediction model based on 2011–2016 data, and then applied the GPS model to calculate the target year distributions of carbon emissions, finally employing a data envelopment analysis (DEA) model to evaluate the effectiveness of these allocations. The carbon trading evaluations and carbon emission reduction potentials of the five major power groups differed; governmental policy must distinguish among these groups. The DEA showed that China Datang Corporation (in 2017, 2020 and 2030), China Huadian Corporation (in 2017, 2020), China Guodian Corporation (in 2017, 2020) and State Power Investment (in 2017, 2020 and 2030) are invalid. Thus, there is much room for improvement in carbon emission efficiency. To ensure reductions in carbon emissions, it is essential to improve the coal consumption efficiencies. China Datang Corporation, China Huadian Corporation, China Guodian Corporation and State Power Investment should reduce coal consumption in 2020; while China Datang Corporation and State Power Investment should reduce coal consumption in 2030. To reduce carbon emissions via rational allocation of quotas, the government must impose higher standards on carbon-Abstract: It is essential to study the allocation of carbon emission quotas to key high-carbon industries. Here, we used the theoretical generation performance standard (GPS) to evaluate the carbon inputs and outputs of the five major Chinese power groups. We predicted their 2017, 2020, and 2030 performances using a grey prediction model based on 2011–2016 data, and then applied the GPS model to calculate the target year distributions of carbon emissions, finally employing a data envelopment analysis (DEA) model to evaluate the effectiveness of these allocations. The carbon trading evaluations and carbon emission reduction potentials of the five major power groups differed; governmental policy must distinguish among these groups. The DEA showed that China Datang Corporation (in 2017, 2020 and 2030), China Huadian Corporation (in 2017, 2020), China Guodian Corporation (in 2017, 2020) and State Power Investment (in 2017, 2020 and 2030) are invalid. Thus, there is much room for improvement in carbon emission efficiency. To ensure reductions in carbon emissions, it is essential to improve the coal consumption efficiencies. China Datang Corporation, China Huadian Corporation, China Guodian Corporation and State Power Investment should reduce coal consumption in 2020; while China Datang Corporation and State Power Investment should reduce coal consumption in 2030. To reduce carbon emissions via rational allocation of quotas, the government must impose higher standards on carbon- and energy-intensive industries; support clean energy by formulating preferential fiscal and tax policies; encourage the development of renewable energy technology; encourage enterprises to strengthen technical cooperation; seek breakthroughs in transition and energy emission reduction technology; and enhance the carbon emission efficiency of electric power enterprises. … (more)
- Is Part Of:
- Energy procedia. Volume 152(2018)
- Journal:
- Energy procedia
- Issue:
- Volume 152(2018)
- Issue Display:
- Volume 152, Issue 2018 (2018)
- Year:
- 2018
- Volume:
- 152
- Issue:
- 2018
- Issue Sort Value:
- 2018-0152-2018-0000
- Page Start:
- 115
- Page End:
- 124
- Publication Date:
- 2018-10
- Subjects:
- Power enterprises -- carbon emissions -- GPS -- DEA -- grey prediction
Power resources -- Congresses
Power resources -- Periodicals
Power resources
Conference proceedings
Periodicals
333.7905 - Journal URLs:
- http://www.sciencedirect.com/science/journal/18766102 ↗
http://www.elsevier.com/journals ↗ - DOI:
- 10.1016/j.egypro.2018.09.068 ↗
- Languages:
- English
- ISSNs:
- 1876-6102
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 3747.729700
British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 11341.xml